Systems and methods for determining shooter locations with weak muzzle detection

ABSTRACT

Systems and methods for locating the shooter of supersonic projectiles are described. The system uses at least five, preferably seven, spaced acoustic sensors. Sensor signals are detected for shockwaves and muzzle blast, wherein muzzle blast detection can be either incomplete coming from less than 4 sensor channels, or inconclusive due to lack of signal strength. Shooter range can be determined by an iterative computation and/or a genetic algorithm by minimizing a cost function that includes timing information from both shockwave and muzzle signal channels. Disambiguation is significantly improved over shockwave-only measurements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 11/210,295, filed Aug. 23, 2005, the entire contents of whichare incorporated herein by reference.

BACKGROUND OF THE INVENTION

The present invention relates to law enforcement technologies andsecurity, and more particularly to methods and systems for determiningthe origin and direction of travel of supersonic projectiles based onshockwave and muzzle sound information. The methods and systems arecapable of determining and disambiguating shooter locations even forlarge distances between shooter and sensor and when the signal from themuzzle sound is weak.

Systems and methods are known that can determine the general directionand trajectory of supersonic projectiles, such as bullets and artilleryshells by measuring parameters associated with the shockwave generatedby a projectile. One such system, described in U.S. Pat. No. 5,241,518includes at least three spaced-apart sensors, with each sensorincorporating three acoustic transducers arranged in a plane. Thesensors generate signals in response to the shockwave which are relatedto the azimuth and elevation angle to the origin of the shockwave.However, shock-wave-only measurements are unable to determine thedistance between the sensor(s) and the origin of the shockwave. Withadditional information from the muzzle blast, an accurate location ofthe origin of the projectile and a line of bearing to the origin of theprojectile can be determined. However, when the muzzle blast is masked,shadowed or otherwise distorted, the signal from the muzzle blast may bedifficult to discern from spurious signals and noise, so that thederived distance information may become inaccurate. Moreover, even if aclearly discernable muzzle blast signal were received from distantshooter locations, determining the range (distance) of the shooter canstill remain a problem.

Conventional systems employ acoustic sensors, e.g., microphones, whichcan be relatively closely spaced (e.g., 1 meter apart) or widelydispersed (e.g., mounted on a vehicle or carried by soldiers on abattlefield), and measure shockwave pressure omni-directionally at theirrespective locations.

The azimuth angle, elevation angle, and range of a shooter withreference to the sensor location can be determined by measuringTime-of-Arrival (TOA) information of the muzzle signal and shockwavesignal at each sensor. Each of the sensors encounters these signals at adifferent time and generates an electric signal in response to theshockwave pressure. The signals from the various sensors are processed,and a direction (azimuth and elevation) from the sensor(s) to the originof the shockwave as well as the range, and hence the trajectory of theprojectile can be determined.

Conventional algorithms require at least 4 shockwave and muzzledetections so that a 4×4 matrix can be inverted to map a plane wave onthe shockwave TOA. Small errors in shock and muzzle TOA determinationcan produce substantial errors in the range estimations. Moreover, theconventional algorithms assume a constant bullet speed along the bullettrajectory, which gives inaccurate range estimates for long-range shotsbeing fired from a distance of more than approximately 300 m.

Accordingly, there is a need for rapidly converging algorithms capableof accurately estimating a distant shooter range.

In addition, there is a need to disambiguate shock-wave only solutionsfor the shooter direction. This is typically accomplished by using themuzzle signal. Disadvantageously, however, less than 4 muzzle sounds maybe detected in many situations, for example, due to extraneous noise,reflections, etc., that can mask the muzzle signal. It would thereforebe desirable to provide a method for reliably disambiguating shock-waveonly solutions even if muzzle blast signals are detected on less than 4,for example 2 or 3, acoustic sensors.

In some situations, an initially detected muzzle signal may be discardedas being unreliable, for example, because the detection level is too lowto indicate a muzzle blast signal; or because the muzzle energy is notreadily evident in the raw signal; or because echoes from the shockwaveare stronger than the muzzle blast and arrive earlier than the actualmuzzle blast, causing the detection system to falsely identify shock asmuzzle. These uncertain muzzle blast signals, if properly identified andextracted, may still be useful for refining detection of the shooterposition, in particular in conjunction with the detection of shock-wavesignals.

It would therefore also be desirable to provide a method for extractingmuzzle signals that may be obscured by acoustic signatures unrelated tothe muzzle blast.

SUMMARY OF THE INVENTION

The invention addresses the deficiencies of the prior art by, in variousembodiments, providing methods and systems for estimating shooter rangefor long-range shots, in particular, when muzzle signals are either weakor detected in an insufficient number of detection channels. Thedisclosed methods and systems also improve disambiguation ofshockwave-only shooter trajectory solutions by including detected muzzlesound in the optimization process.

According to one aspect of the invention, in a method for estimating ashooter range by detecting shock wave and muzzle blast, shockwave-onlysignals as well as muzzle blast signals are measured at a plurality ofspaced acoustic sensors forming an antenna. An initial shooter range isestimated from the measured shock wave and muzzle blast signals,assuming an initial bullet velocity and a bullet drag coefficient. Theinstantaneous bullet velocity along a bullet trajectory is iterativelycomputed to obtain an updated shooter range. The number of muzzle blastdetection channels is usually less than the number of shockwavedetection channels.

Advantageous embodiments may include one or more of the followingfeatures. A time-difference-of-arrival (TDOA) between the shockwave-onlysignals and the muzzle blast signals and an arrival angle are computedfor determining the initial shooter range. A certain number ofiterations may be performed, or the updated shooter range will beconsidered to be the final shooter range if a relationship betweensuccessively determined updated shooter ranges satisfies the convergencecriterion. For example, the convergence criterion may be selected sothat the difference between the successively determined updated shooterranges or a percentage change between the successively determinedupdated shooter ranges is smaller than a predetermined value. To obtainreal solutions, the computed bullet velocity is set to always be atleast the speed of sound. The solutions are checked for consistency. Forexample, the updated shooter range is considered invalid if a bullettrajectory angle and an arrival angle are determined to be greater thana predetermined value.

Even if the computed shooter range is determined to be invalid, asolution may still be obtained by applying a genetic algorithm (GA). Forexample, an initial population of the GA with a predetermined number ofindividuals can be defined, where each individual is represented by a3-tupel which includes an assumed shooter range, a missed azimuth (MA)and a missed elevation (ME) of the bullet trajectory. The GA isperformed for a predefined number of generations, and residuals for theindividuals in each generation are computed. In each generation thesolution with the smallest residual is selected as the individual whichsurvives unmutated. The solution having the smallest residual isselected as the updated shooter range. The solution can be refined byperforming for each 3-tupel in a generation a predetermined number ofiterations to compute a revised shooter range, wherein the residuals forthe individuals in each generation are computed with the revised shooterrange.

The GA includes crossover and mutation operators. The crossover operatorexchanges at least one of missed azimuth and missed elevation betweentwo individuals from the population in a generation, whereas themutation operator comprises field-mutation (replacing a value of the3-tupel with a randomly selected value), incremental mutation (inducinga small mutation in all fields of the 3-tupel), and no mutation (leavingthe individuals in a generation unaltered).

According to yet another aspect of the invention, a method fordisambiguating a projectile trajectory from shockwave signals and from alimited number of muzzle blast signals includes measuring shockwave-onlysignals at five or more spaced acoustic sensors forming an antenna,measuring muzzle blast signals on at most 4 of the sensors, anddetermining from the shockwave-only signals Time-Differences-Of-Arrival(TDOA) information for sensor pairs. The method further includesperforming for a predefined number of generations a genetic algorithmwith an initial population that includes a predetermined number ofindividuals, each individual represented by a 4-tupel which includesshooter azimuth, shooter elevation, missed azimuth and missed elevation,and computing residuals for the individuals in each generation, with theresiduals including a least-square fit of a combination of TDOAshockwave and muzzle blast signals. If a ratio of the solution havingthe smallest residual and its ambiguous alternate solution is greaterthan a predefined value, the solution having the smallest computedresidual is designated as the disambiguated projectile trajectory.

According to another aspect of the invention, a method for extracting asignal from a muzzle wave in the presence of a shockwave signal includesdefining a time window having a width corresponding to a time requiredfor a muzzle wave to traverse a sensor array and detecting the shockwavesignal. Following detection of the shockwave signal, the window isadvanced in time and the total energy received in the window is measuredas a function of advance time. The maximum of the measured total energyis associated with the muzzle signal.

In advantageous embodiments, the total energy can be determined byintegrating the measured energy over the window, preferably disregardingportions in the detected signal caused by shockwave echoes.Advantageously, the peak signal value can be determined in the windowproducing the maximum total energy and if the peak signal value isgreater than the measured total energy in the window by a predefinedratio factor, the peak signal value can be identified as being relatedto the muzzle signal.

Further features and advantages of the invention will be apparent fromthe following description of illustrative embodiments and from theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the invention will be morefully understood by the following illustrative description withreference to the appended drawings, in which elements are labeled withlike reference designations and which may not be to scale.

FIG. 1 is a schematic diagram of a shockwave Time-of-Arrival (TOA)model;

FIG. 2 shows a schematic process flow diagram for range estimation;

FIG. 3 shows a schematic process flow diagram of a genetic algorithm forrange estimation; and

FIG. 4 shows a schematic process flow diagram of a genetic algorithm fordisambiguating between shooter trajectories.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

As described above in summary, the invention provides, in variousembodiments, methods and systems for shooter range estimation anddisambiguation of projectile trajectories. These systems and method areparticularly useful and advantageous, when an insufficient number ofparameters required for an accurate solution are detected or when suchparameters cannot be detected reliably.

FIG. 1 shows schematically a diagram of a Time of Arrival (TOA) model,which is described in more detail in U.S. Pat. No. 6,178,141(incorporated herein by reference in its entirety). The TOA model can beused to estimate the trajectory of the projectile and the shooterdirection relative to the sensor location. The TOA model is based on aballistic model taking into account certain physical characteristicsrelating to the projectile's flight path, such as the air density (whichis related to temperature); the position {right arrow over (P)} (P_(x),P_(y), P_(z)) of a shooter; the azimuth and elevation angles of therifle muzzle; the muzzle velocity of the projectile (or equivalent Machnumber); and the speed of sound (which varies with temperature/airdensity). With this ballistic model, it is possible to accuratelycalculate the time at which the shockwave and muzzle blast reach aparticular point in space.

As depicted in the diagram of FIG. 1, the shooter is located at point{right arrow over (P)} (P_(X), P_(Y), P_(Z)) relative to an origin (0,0, 0), the various sensors are located at points {right arrow over(S)}_(j) (S_(xj), S_(yj), S_(zj)) and the bullet trajectory is shown asemanating from the shooter in the direction of {right arrow over (A)}.The vector from the shooter to the j^(th) sensor is {right arrow over(D)}, the closest point of approach (CPA) of the bullet to the j^(th)sensor is |{right arrow over (R)}|=|{right arrow over (D)}|sin (β), andthe path followed from the point where the shockwave is radiated fromthe trajectory to the j^(th) sensor is {right arrow over (S)} (the indexj of the sensors has been omitted). The Mach angle of the bullet isθ=sin⁻¹ (1/M), M=V/c₀. M is the Mach number of the projectile, V is thesupersonic velocity of the projectile, and c₀ is the (pressure- andtemperature-dependent) speed of sound. The ‘miss-angle’ betweentrajectory and the j^(th) sensor is β. The trajectory is characterizedby its azimuth angle measured counter-clockwise from the x-axis in thex-y plane and by its elevation angle measured upward from the x-y plane.The equations that define the shockwave arrival time t_(j) and unitvector at the j^(th) sensor are written in terms of these geometricalquantities.

The time of arrival is equal to the time

$\frac{\overset{\rightarrow}{A}}{V}$

it takes for the projectile to travel the distance |{right arrow over(A)}| to the point were sound is radiated toward the j^(th) sensor, plusthe time it takes the shockwave to travel the distance |{right arrowover (S)}| from the radiation point to the j^(th) sensor,

$\frac{\overset{\rightarrow}{S}}{c_{0}}.$

$\begin{matrix}{{t_{j} = {{t_{0} + \frac{A}{V} + \frac{S}{c_{0}}} = {t_{0} + {\frac{D}{c_{0}}{\sin \left( {\beta + \theta} \right)}}}}},} & (1)\end{matrix}$

wherein t₀ is a time reference (firing time) and c₀ is the speed ofsound. The Mach angle Θ is also indicated in FIG. 1.

It can be safely assumed that the velocity V of the projectile remainsconstant over a distance corresponding to the sensor spacing, so thatthere is insignificant loss of speed between the times the projectileradiates to the different sensors. However, over longer distancesprojectiles are known to slow down due to air resistance. The airresistance can be expressed by a drag coefficient C_(b) which depends onthe bullet shape and bullet caliber. A mathematical ballistics modelderived from physical principles can predict the arrival time of ashockwave at any general point in space as a function of a full set ofparameters describing the projectile (e.g., by its drag coefficientC_(b)), its initial velocity, and the density of the surrounding air areknown in advance.

The parameters required for an exact calculation are typically not knownin a realistic setting, such as a battlefield. However, range estimationcan be significant improved by an iterative process shown in form of aprocess flow diagram 20 in FIG. 2, which takes into account decelerationof the projectile velocity along the trajectory. The process 20 beginsat step 202 with the following assumptions:

c₀ speed of sound modified for outside temperature/air pressure (≈340m/s)

C_(b)=nominal drag coefficient averaged over anticipated weapons

V₀=initial velocity of the projectile, when fired, averaged overanticipated weapons

M₀=V₀/c=initial Mach number of projectile

A first estimate of the shooter distance D₀ is computed in step 204using the measured time-difference-of-arrival (TDOA) τ_(ms) and anarrival angle α between shock and muzzle sound at the sensor array andby assuming an initial, constant speed V₀ and Mach number M₀, accordingto the equation

$\begin{matrix}{D_{0} = \frac{\tau_{m\; s} \cdot c_{0}}{1 - {\cos (\alpha)}}} & (2)\end{matrix}$

With these assumptions, the projectile's speed at a distance a from theshooter position {right arrow over (P)} can be computed in step 206 fromthe equation

$\begin{matrix}{M_{a} = {M_{0}\left( {1 - \frac{a}{C_{b}\sqrt{V_{0}}}} \right)}^{2}} & (3)\end{matrix}$

so that the time the projectile travels the distance a along thetrajectory becomes, step 208,

$\begin{matrix}{T_{a} = \frac{a}{V_{0} - \frac{a \cdot \sqrt{V_{0}}}{C_{b}}}} & (4)\end{matrix}$

The angle Θ is related to the Mach number M_(a) by the equation

$\begin{matrix}{{\sin (\Theta)} = \frac{1}{M_{a}}} & (5)\end{matrix}$

wherein the Mach number M_(a) is initially set to M₀. It should be notedthat the instantaneous bullet velocity is set to the speed of sound(i.e., M_(a)=1) if the computed bullet velocity becomes less than thespeed of sound. The revised distance a=|{right arrow over (A)}| in step210 then becomes

$\begin{matrix}{a = {D_{0} \cdot {\cos (\beta)} \cdot {\left( {1 - \frac{\tan (\beta)}{\sqrt{M_{a}^{2} - 1}}} \right).}}} & (6)\end{matrix}$

The angles α, ⊖, and Θ are related by the equation (α+β+Θ))=90°. Theprocess 20 then loops back to step 206 by inserting the computed valuefor the distance a in equations (3) and (4), yielding an updated Machnumber M_(a) and an updated bullet travel time T_(a), respectively, forthe traveled distance a. The measured TDOA τ_(ms) and the computedupdated values for T_(a) and a are then used to successively update thevalue D for the shooter range:

D=c ₀·(τ_(ms)+T _(a))+s   (7)

This process is repeated until either a maximum number of iterations hasbeen reached or the range values D converge, as determined in step 212.

The process 20 also checks in step 214, if the revised range valueD=|{right arrow over (D)}| for the distance between the shooter and thesensor array is a “reasonable” value, in which case the process 20terminates at step 216. For example, the value for D can be consideredvalid if the distance a traveled by the projectile and the distance

$s = {a \cdot \frac{\sin (\beta)}{\sin (\alpha)}}$

between the sensor and the point where the sound wave is radiated fromthe projectile to the sensor are valid number values, i.e., not a NAN. ANAN is a special floating point value representing the result of anumerical operation which cannot return a valid number value and istypically used to prevent errors from propagating through a calculation.In addition, α and β should both be less than a predetermined thresholdvalue, indicating that the projectile was indeed fired toward the sensorarray.

As mentioned above, the number pair (τ_(ms), α) is initially used tocompute the shooter range D₀ in zeroth approximation, neglecting changein the projectile's speed along the trajectory. If the iterative process20 described above does not return a consistent geometry supporting thenumber pair (τ_(ms), α), then the solution is discarded.

Even if an exact solution may not be obtainable, it is a goal to findvalues for the shooter range D and the missed azimuth and elevationangles (which are related to β) that most closely match a measured shockTDOA and a measured muzzle TDOA. As already mentioned, theshockwave-only TDOA's between the various sensors can in most situationsbe reliably measured. The shooter azimuth and shooter elevation, but notthe shooter range, can be determined from the shockwave-only TDOA'susing the known sensor array coordinates (S_(xj), S_(yj), S_(zj)). Itwill be assumed that the TDOA τ_(ms) between the detected shockwave andthe muzzle sound can also be measured, whereby the muzzle sound may notbe detected by all the sensors.

If it is determined in step 214 that the iterative process 20 does notreturn a valid result, then the process 20 attempts to compute theshooter range by invoking an evolutionary genetic algorithm (GA) 30.GA's mimic natural evolutionary principles and apply these to search andoptimization procedures. A GA begins its search with a random set ofsolutions, instead of just one solution. Once a random population ofsolutions is created, each is evaluated in the context of the nonlinearprogramming problem and a fitness (relative merit) is assigned to eachsolution. In one embodiment, the fitness can be represented by theEuclidean distance between a calculated solution and the measuredsolution, for example, by

$\begin{matrix}{{\Delta \; \tau_{\min}} = {\sqrt{\frac{\sum\limits_{j = 1}^{N}\; \left( {\tau_{{Shock},{calc}}^{j} - \tau_{{Shock},{meas}}^{j}} \right)^{2}}{N}} + {{abs}\left( {{\sum\limits_{i = 1}^{N}\; \left( \frac{\tau_{{m\; s},{calc}}^{i}}{M} \right)} - \tau_{{m\; s},{meas}}} \right)}}} & (8)\end{matrix}$

Intuitively, an algorithm producing a smaller value of Δτ_(min) isbetter.

A schematic flow diagram of the GA process 30 is shown in FIG. 3. Theprocess 30 uses the time-difference-of-arrival (TDOA) τ_(ms) and thearrival angle a measured previously for process 20, step 302. Anexemplary number of 3-tupes having the values {RANGE, MA, ME} is definedas an initial population, step 304, wherein RANGE is the shooter rangeD=|{right arrow over (D)}| shown in FIG. 1, MA is the missed azimuth,and ME is the missed elevation. The MA and ME values indicate by howmuch the bullet missed the target in azimuth and elevation space. Thetarget in the illustrate example is assumed to be the sensor array. Theinitial population in step 304 is created by random selection of the3-tupel spanning a meaningful and reasonable range of values:

Range_(Shooter)={1000, . . . , 3000} [meter],

Azimuth_(Missed)={−20, . . . , 20} [degrees], and

Elevation_(Missed)={−20, . . . , 20} [degrees].

The computation essentially follows the same process as outlined above.Initially, for generation Gen=0, step 306, the shooter position vector{right arrow over (P)} (P_(x), P_(y), P_(z)) is computed for each3-tupel with the previously determined shooter azimuth and elevation,and an assumed RANGE for the particular 3-tupel. Assuming an initialMach number M₀, the vector {right arrow over (A)} (A_(x), A_(y), A_(z)),i.e. the position from where the shock sound is radiated, is computedwith the MA and ME values for each 3-tupel, step 308. The distances{right arrow over (D)}={right arrow over (S)}_(j)−{right arrow over (P)}between the shooter and each sensor j that detects a shockwave are alsocomputed.

For each 3-tupel, the angle β is computed from the equation cos

${\beta = \frac{\overset{\rightarrow}{A} \cdot \left( {\overset{\rightarrow}{P} - \overset{\rightarrow}{S}} \right)}{{\overset{\rightarrow}{A}} \cdot {{\overset{\rightarrow}{P} - \overset{\rightarrow}{S}}}}},$

wherein the symbol “” indicates the scalar product between the twovectors. Updated values for the distance a, the travel time T_(a) of theprojectile over the distance a, and the Mach number M_(a) are computedby inserting the computed value for β and the initially assumed valuesfor M_(a)=M₀ and a into equations (3), (4), (6), and (7), step 312. Thisprocess is iterated several times for each of the 3-tupels, for example3 times, as determined in step 312, whereafter the residual defined inequation (8) is computed for each 3-tupel, step 314.

It is checked in step 316 if a maximum number of iterations for the GA,for example 25 iterations, have been reached. If the maximum number ofiterations has been reached, then the process 30 stops at step 320,returning the 3-tupel with the smallest residual. Otherwise, the process30 creates a new population through a crossover and mutation operation,step 318, and the generation counter is incremented by one, step 322 Ineach generation, the “best” individual is allowed to survive unmutated,whereas the top 100 individuals, as judged by their fitness, alsosurvive, but are used to create the next 100 individuals from pairs ofthese survivors with the crossover/mutation operators listed in Table 1below.

The following exemplary crossover and mutation operators were used todemonstrate the process 30:

TABLE 1 Operator Operator Name Type Probability Description MissedAzimuth- Crossover 0.5 Exchange missed azimuth between two Crossoverchromosomes Missed- elevation Crossover 0.5 Exchange missed elevationbetween Crossover two chromosomes Missed- range Crossover 0.5 Exchangerange between two Crossover chromosomes Field-Mutation Mutation 0.3Replace a given field (with a probability of 0.25 per field) with arandomly selected new value within range Incremental-Mutation Mutation0.4 Introduce small perturbations in all fields of a chromosome (within±2 meter for shooter range; within ±0.1° for missed azimuth andelevation)

The GA process 30 is executed with an initial population of 200different 3-tupels, with a refill rate of 50, for a total of 25generations. The GA is run 5 times in parallel with different sets ofinitial 3-tupels, and the solution with the smallest residual isselected as the final solution for the RANGE, missed azimuth, and missedelevation of the shooter, which allows computation of vector {rightarrow over (D)}.

Calculations that do not take into consideration the deceleration of theprojectile along its path due to air resistance tend to overestimaterange. For certain geometries and sufficiently distant shots, thisoverestimation may exceed 20%. The aforedescribed process removes thisbias from range estimation for long-range shot detections.

Although shock-wave only solutions may be ambiguous, they can frequentlybe disambiguated by comparing the residuals from two differenttrajectories and selecting the trajectory with the smaller residual.

The residual of the shockwave-only solution can be defined as

$\begin{matrix}{{\Delta\tau}_{\min} = \sqrt{\sum\limits_{j}\left( {{\Delta\tau}_{{Shock},{calc}}^{j} - {\Delta\tau}_{{Shock},{meas}}^{j}} \right)^{2}}} & (9)\end{matrix}$

summed over all sensors j. The solution with the smallest value forΔτ_(min) is selected as the most likely solution for the shooterdirection. The precision of disambiguation can be improved, for example,by employing a genetic algorithm GA of the type disclosed, for example,in commonly assigned U.S. patent application Ser. No. 10/925,875.

If the muzzle blast signals are detected on 4 or more sensor channels,then the aforedescribed shock-muzzle algorithms can be used tounambiguously determine the shooter location, regardless of the numberof shock channels. If the muzzle blast signals are detected in fewerthan 4 sensors, but shockwave signals are detected in 5 or moreshockwave channels, then the aforementioned GA can be used with amodified cost function or residual, whereby whatever muzzle signals areavailable are “mixed” into the optimization function to disambiguate theshockwave-only solution and/or refine the estimation of the shooterrange. However, if fewer than 3 muzzle channels AND fewer than 5shockwave channels are detected, then an alert can be activated withoutattempting to localize the shooter.

The muzzle signal may not be reliably detected on all channels, because:

-   -   1. The detection level on one or more channels is too low to        detect with confidence.    -   2. The muzzle energy is not discernable in the raw signal,        causing the system to correlate with ‘noise’, giving unreliable        TDOA estimates.    -   3. Echoes from the shockwave can be stronger than the muzzle        blast and can arrive earlier than the muzzle blast, causing the        system to falsely detect shock as muzzle.

With a muzzle blast signal only detected on some channels, the residualin this situation can be defined as

$\begin{matrix}{{{\Delta\tau}_{\min} = \sqrt{\begin{matrix}{{\sum\limits_{i}\left( {{\Delta\tau}_{{muzzle},{calc}}^{i} - {\Delta\tau}_{{muzzle},{meas}}^{i}} \right)^{2}} +} \\{\sum\limits_{j}\left( {{\Delta\tau}_{{Shock},{calc}}^{j} - {\Delta\tau}_{{Shock},{meas}}^{j\;}} \right)^{2}}\end{matrix}}},} & (10)\end{matrix}$

wherein the first term for the muzzle blast is summed over the reducednumber of sensors (<4) that detect the muzzle blast, and j is summedover the sensors detecting the shockwave (typically all sensors).

When applying the GA to disambiguate the shooter direction andprojectile trajectory under these circumstances, the exemplary GA usesas a chromosome an initial population of 200 4-tupels, with each 4-tupelcontaining the following values:

[Azimuth_(Shooter), Elevation_(Shooter), Azimuth_(Missed),Elevation_(Missed)].

The values [Azimuth_(Shooter), Elevation_(Shooter)] are defined by theangle (90°−(Θ+β)), while the values [Azimuth_(Missed),Elevation_(Missed)] are defined by the angle β (see FIG. 1). An initialrange of, for example, 1000 m can be assumed for the shooter range.

The initial population is created by random selection of the 4-tupelsspanning a meaningful and reasonable range of values (all values are indegrees):

Azimuth_(Shooter)={0, . . . . , 360},

Elevation_(Shooter)={−10, . . . , 30},

Azimuth_(Missed)={−20, . . . , 20}, and

Elevation_(Missed)={−20, . . . , 20}.

A schematic flow diagram of the GA process 40 is shown in FIG. 4. The GAprocess 40 begins its search by initializing a random population ofsolutions, step 402, and sets a generation counter to zero indicatingthe initial solution set, step 404. Once a random population ofsolutions is created, each is evaluated in the context of the nonlinearprogramming problem, step 406, and a fitness (relative merit) isassigned to each solution, step 408. The fitness can be represented bythe residual Δτ_(min) in between a calculated solution and the measuredsolution, as described above. It is checked in step 410 if a maximumnumber of iterations for the GA, which can be set, for example, to 25,has been reached. If the maximum number of iterations has been reached,the process 40 stops at step 420, and the result can be either acceptedor further evaluated. Otherwise, step 412 checks if preset fitnesscriteria have been satisfied.

Fitness criteria can be, for example, a computed missed azimuth of <15°and/or a ratio of the residuals of two ambiguous solutions. If thefitness criteria are satisfied, the process 40 stops at step 420;otherwise, a new population is created through crossover/mutations, step414, and the generation counter is incremented by one, step 416.

In each generation, the “best” individual is allowed to surviveunmutated, whereas the top 100 individuals, as judged by their fitness,also survive, but are used to create the next 100 individuals from pairsof these survivors with the crossover/mutation operators listed in Table2.

The following exemplary crossover and mutation operators were used todemonstrate the process 40:

TABLE 2 Operator Operator Name Type Probability DescriptionAzimuth-Crossover Crossover 0.5 Exchange shooter/trajectory azimuthbetween two chromosomes Missed-Crossover Crossover 0.5 Exchange missedazimuth/elevation between two chromosomes Field-Mutation Mutation 0.3Replace a given field (with a probability of 0.25 per field) with arandomly selected new value within range Incremental-Mutation Mutation0.4 Introduce small mutations in all fields of a chromosome (within ≦2°for shooter information; within ≦0.5° for missed informationFlip-Mutation Mutation 0.1 Change the solution into the ambiguousalternate solution No-Mutation Mutation 0.2 Chromosome remains intact

Recent experimental trials indicated a decrease of ambiguous shots from95% to 8% on the same data set by using at least one muzzle signalchannel in addition to 5 or more shockwave channels.

As demonstrated by the examples described above, the muzzle blast signalprovides important information about the shooter azimuth and hence theprojectile's trajectory as compared to a shockwave-only solution, sothat the computed trajectory solution aligns more closely with one ofthe ambiguous solutions, i.e., thus disambiguating the solutions.

Without at least some reliable muzzle signals, a significant number ofambiguous shockwave-only solutions may be generated in particular atlong shooter distances, which are less desirable than a smaller numberof unambiguous, but less precise solutions.

In the event of potentially unreliable muzzle detection, an attempt canstill initially be made to detect muzzle signals, e.g., to find a muzzleblast signature in a noisy signal, and to compute the resulting TDOA's.The muzzle detection will be deemed to be reliable if muzzle signals arefound on a sufficient number of sensor with sufficient cross-correlationbetween the channels, and if there is a sufficiently strong correlationbetween the muzzle signal and the corresponding raw band on each channel(offset by a number of bins to account for filter delays).

Otherwise, at least the muzzle signals that show insufficientcorrelation are erased, and the following ‘coarse muzzle detection’logic is invoked:

-   -   Look for peaks in the shock energy following a shock. Flag these        peaks as likely ‘shock echoes’, thereby excluding them as muzzle        blasts.    -   Determine a maximum time it would take the muzzle wave to        traverse the sensor array and define a “window” having the        corresponding duration. Search for muzzle energy peaks by moving        this window across substantially all detector channels following        the detected shockwave, skipping sections in the detected signal        which have been identified as shock echoes. Integrate the energy        over the window, i.e., seek the maximum of:

$\begin{matrix}{{f(i)} = {\sum\limits_{n = 0}^{N}\; {\sum\limits_{j = 0}^{W}\; \left( {mb}_{i + j}^{n} \right)^{2}}}} & (11)\end{matrix}$

wherein the square of mb_(i+j) ^(n) represents a measure of energy,e.g., energy of the muzzle blast, measured by the n^(th) sensor. (i+j)indicates the detection channel, with i denoting a discrete timeinterval between the time the shockwave was detected and the beginningof the window, and j represents a time interval measured from the startof the window.

To discriminate against noise, the peak energy in the window producingthe maximum of function ƒ_(max)(i) in equation 11 is checked todetermine if the energy peak at that maximum is greater than the energyacross the window by a given ratio factor. If this is the case, then thesignal in the window is identified as a muzzle detection, andcross-correlation is performed on all channels in the muzzle blast bandmb to determine muzzle TDOA's.

The detected muzzle blast signal can then be used to determine theshooter range and/or disambiguate the shockwave signal, as describedabove.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon may be made thereto without departing from thespirit and scope of the invention. By way of example, although theillustrative embodiments are depicted as having acoustic sensors, suchas microphones, this need not be the case. Instead, other types ofpressure-sensitive mechanical or electrical sensors may be used.Moreover, the values given in Tables 1 and 2 for the various operatorsare intended to be examples only, and other values may be selecteddepending on the actual conditions in the field. Accordingly, the spiritand scope of the present invention is to be limited only by thefollowing claims.

1. A method for disambiguating a projectile trajectory from shockwavesignals and a limited number of muzzle blast signals, comprising:measuring shockwave-only signals at five or more spaced acoustic sensorsforming an antenna; measuring muzzle blast signals on at most four ofthe sensors; determining from the shockwave signalsTime-Differences-Of-Arrival (TDOA) information for sensor pairs; andcomputing the disambiguated projectile trajectory from the TDOAinformation, shockwave signals, and muzzle blast signals.
 2. The methodof claim 1, wherein muzzle blast signals are measured on exactly threeof the sensors.
 3. The method of claim 2, wherein the disambiguatedprojectile trajectory is computed using a genetic algorithm.
 4. Themethod of claim 3, wherein performing the genetic algorithm comprisesusing a predefined number of generations with an initial population thatincludes a predetermined number of individuals, wherein each individualrepresented by a 4-tupel which includes shooter azimuth, shooterelevation, missed azimuth and missed elevation.
 5. The method of claim4, wherein residuals are computed for the individuals in eachgeneration, said residuals including a least-square fit of a combinationof TDOA shockwave and muzzle blast signals.
 6. The method of claim 5,wherein if a ratio of the solution having the smallest residual and itsambiguous alternate solution is greater than a predefined value,designating the solution having the smallest computed residual as thedisambiguated projectile trajectory.
 7. The method of claim 6, whereinperforming the genetic algorithm comprises applying crossover andmutation operators to the initial population in a generation.
 8. Themethod of claim 7, wherein applying the crossover operator includesexchanging at least one of missed azimuth and missed elevation betweentwo individuals from the population in a generation.
 9. The method ofclaim 7, wherein applying the crossover operator includes exchanging atleast one of shooter azimuth and trajectory azimuth between twoindividuals from the population in a generation.
 10. The method of claim7, wherein the mutation operator comprises field-mutation, incrementalmutation, flip-mutation and no mutation.
 11. The method of claim 10,wherein the field-mutation operator replaces a value of the 4-tupel witha randomly selected value.
 12. The method of claim 10, wherein theincremental mutation operator induces a small mutation in all fields ofthe 4-tupel.
 13. The method of claim 10, wherein the flip-mutationoperator changes the solution in a generation to its ambiguous alternatesolution in the generation.
 14. The method of claim 10, wherein theno-mutation operator leaves the individuals in a generation unaltered.